1. Field of the Invention
The present invention relates to information retrieval systems, and in particular, to a system and method of information retrieval that utilize progressive feature selection and submission. Still more particularly, the present invention relates to a visual search system and method that uses progressive feature selection and submission.
2. Description of the Background Art
The use of portable computing devices has become commonplace for millions of users. In addition to having a conventional desktop computer, many users also have some type of portable computing device. Smaller portable computing devices include a plethora of smart phones including the iPhone from Apple Inc., the Blackberry from Research In Motion and the Pre from Palm just to name a few. There have also been increased sales of notebook laptops and tablet computers offering different levels of processing capability and size.
These new devices are now often used for visual search. Visual search technology links the physical world to the digital world by matching the visual appearance of objects against a database. The most popular visual search architecture 100 as of today is client-server, as shown in FIG. 1: the client (typically a camera or smart phone 204) acquires an image 106 of a document 102, submits that image 106 to a server 108, the server 108 runs image matching algorithm (usually involves feature extraction, candidate selection and geometrical verification) and then return the closest match to the client 204.
The problem with this architecture 100 is network latency. When the user submits an image to the server 108 it may take 5˜10 seconds to finish the upload depending on the network. It is especially power consuming and slow to maintain an upstream connection over the phone network. While waiting for the upload to finish the user often loses patience and blames the program as not working. This is even worse if the search query results do not match, which is often the case because the submitted image is blurry, dark or empty.
There have been attempts in the prior art to solve the above issues, but they have not been successful. For example, some have attempted to solve the latency problem with a new architecture that moves the feature extraction step to the client side. Instead of the server, the phone runs feature extraction and submits the extracted feature vectors to the server. However, some of the client side devices such a smart phone have limited computational capabilities so latency continues to be an issue. Furthermore, this architecture accelerates the submission only by the difference between the size of the feature vectors and the image itself, which in many cases is not enough to eliminate the problems of network latency.